Storage device lifetime monitoring system and storage device lifetime monitoring method thereof

ABSTRACT

A storage device lifetime monitoring system for monitoring lifetimes of storage devices and a storage device lifetime monitoring method thereof are provided. The method includes collecting operation activity information corresponding to the storage devices; storing multiple training data having the operation activity information and corresponding operation lifetime values; constructing a storage device lifetime predicting model according to the operation activity information and the corresponding operation lifetime values of the training data; inputting the operation activity information of the storage devices into the storage device lifetime predicting model to generate a predicted lifetime value corresponding to each of the storage devices; and re-constructing the storage device lifetime predicting model according to operation activity information and predicted lifetime value of each storage device. Thereby, the lifetime of storage devices can be accurately predicted.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwan applicationserial no. 104103877, filed on Feb. 5, 2015. The entirety of theabove-mentioned patent application is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND

Field of the Invention

The present invention is directed to a storage device lifetimemonitoring system and more particularly, to a storage device lifetimemonitoring system and a storage device lifetime monitoring methodthereof for monitoring a plurality of storage devices of a data center.

Description of Related Art

In recent years, with the continuous progress of the technologies, thesignificant increase in the amount of data has brought the technologyindustry with influence on demands of data storage hardware. Because thelarge-volume data needs to be stored by using a plurality ofnon-volatile storage devices, sizes of the capacities of the storagedevices and complexity of managing the storage devices arecorrespondingly increased.

Generally, in order to manage an operation situation of a data centerfor security maintenance, when a server system for managing the datacenter is developed and designed, a management module is usuallyconfigured to monitor internal information of a system, such as a fanoperation condition, a temperature or a voltage. Thereby, the serversystem can passively take actions, such as data restore or hardwarereplacement, after a report (e.g., a log file corresponding to eachstorage device) with respect to abnormal states of each storage devicein the system is received.

Due to the large capacity of each storage device in the data center,after a storage device is damaged (or encounters a serious error), aduration required for recovering the damaged storage device or backingup the data is significantly increased, which leads to dramatic increaseof maintenance cost for the data center. However, along with thedevelopment of the trend toward high-speed data accessing, the datacenter also introduces with storage devices (e.g., solid-state disks(SSD) capable of high-speed data accessing) rather than the conventionalhard disks (HDD) for storing data. Therefore, the conventionself-monitoring analysis and report technique that is merely adapted forthe conventional HDDs cannot satisfy various kinds of maintenance needsof the storage devices of the data center. Accordingly, how toaccurately predict lifetimes of the storage devices, actively predictthe lifetimes of the storage devices in advance and take preventiveactions to save numerous maintenance cost caused by device damages hasbecome a target for the persons of the art to make effort to.

SUMMARY

The present invention provides a storage device lifetime monitoringsystem and a storage device lifetime monitoring method thereof, capableof effectively predicting lifetimes of storage devices.

According to an exemplary embodiment of the present invention, a storagedevice lifetime monitoring system for monitoring lifetimes of aplurality of storage devices is provided. The storage device lifetimemonitoring system includes a storage device detecting and analyzingmodule, a database, a lifetime estimation training module and a lifetimepredicting module. The database is coupled to the storage devicedetecting and analyzing module. The lifetime estimation training moduleis coupled to the storage device detecting and analyzing module. Thelifetime predicting module is coupled to the storage device detectingand analyzing module and the lifetime estimation training module. Thedatabase records a plurality of training data, wherein each of thetraining data includes operation activity information and acorresponding operation lifetime value. The storage device detecting andanalyzing module collects the operation activity informationcorresponding to the storage devices. The lifetime estimation trainingmodule constructs a storage device lifetime predicting model accordingto the operation activity information and the corresponding operationlifetime value of each of the training data. The lifetime predictingmodule inputs the operation activity information of the storage devicesto the storage device lifetime predicting model to generate a predictedlifetime value corresponding to each of the storage devices.

In an exemplary embodiment of the present invention, the lifetimeestimation training module re-constructs the storage device lifetimepredicting model according to the operation activity information and thepredicted lifetime value of each of the storage devices.

In an exemplary embodiment of the present invention, when a firststorage device among the storage devices is damaged, the storage devicedetecting and analyzing module records an actual lifetime value of thefirst storage device, and the lifetime estimation training modulere-constructs the storage device lifetime predicting model according tothe operation activity information and the actual lifetime value of thefirst storage device.

In an exemplary embodiment of the present invention, the storage devicedetecting and analyzing module includes a log collecting module and anoperation activity identifying module, and in the operation of thestorage device detecting and analyzing module collecting the operationactivity information corresponding to the storage devices, the logcollecting module collects at least one operation log corresponding toeach of the storage devices, and the operation activity identifyingmodule analyzes the at least one operation log of each of the storagedevices to establish the operation activity information of each of thestorage devices.

In an exemplary embodiment of the present invention, the at least oneoperation log comprises a system log, an application log, a database logand a self-monitoring analysis and report technical log.

In an exemplary embodiment of the present invention, the operationactivity identifying module identifies system access errors from thesystem log, application access errors from the application log, databaseaccess errors from the database log and disk access errors in theS.M.A.R.T. log for each of the storage devices, calculates the number ofthe system access errors, the number of the application access errors,the number of the database access errors and the number of the diskaccess errors and establishes the operation activity information of eachof the storage devices according to the number of the system accesserrors, the number of the application access errors, the number of thedatabase access errors and the number of the disk access errors.

In an exemplary embodiment of the present invention, in the operation ofthe lifetime estimation training module constructing the storage devicelifetime predicting model according to the operation activityinformation and the corresponding operation lifetime value of each ofthe training data, the lifetime estimation training module constructsthe storage device lifetime predicting model by means of aK-means-clustering algorithm, a linear regression algorithm or a supportvector machine (SVM).

In an exemplary embodiment of the present invention, in the operation oflifetime estimation training module constructing the storage devicelifetime predicting model according to the operation activityinformation and the corresponding operation lifetime value of each ofthe training data, the lifetime estimation training module splits thetraining data and a plurality of predicting data composed of theoperation activity information and the predicted lifetime value of eachof the storage devices, into a plurality of data sets, constructs aplurality of sub predicting models respectively according to the datasets, and merges the sub predicting models to form the storage devicelifetime predicting model.

According to an exemplary embodiment of the present invention, a storagedevice lifetime monitoring method for monitoring lifetimes of aplurality of storage devices is provided. The storage device lifetimemonitoring method includes establishing a database recording a pluralityof training data, wherein each of the training data includes operationactivity information and a corresponding operation lifetime value; andcollecting the operation activity information corresponding to thestorage devices. The storage device lifetime monitoring method furtherincludes constructing a storage device lifetime predicting modelaccording to the operation activity information and the correspondingoperation lifetime value of each of the training data; and inputting theoperation activity information of the storage devices to the storagedevice lifetime predicting model to generate a predicted lifetime valuecorresponding to each of the storage devices.

In an exemplary embodiment of the present invention, the storage devicelifetime monitoring method further includes re-constructing the storagedevice lifetime predicting model according to the operation activityinformation and the predicted lifetime value of each of the storagedevices.

In an exemplary embodiment of the present invention, the storage devicelifetime monitoring method further includes when a first storage deviceamong the storage devices is damaged, recording an actual lifetime valueof the first storage device; and re-constructing the storage devicelifetime predicting model according to the operation activityinformation and the actual lifetime value of the first storage device.

In an exemplary embodiment of the present invention, the step ofcollecting the operation activity information corresponding to thestorage devices includes collecting at least one operation logcorresponding to each of the storage devices; and analyzing the at leastone operation log of each of the storage devices to establish theoperation activity information of each of the storage devices.

In an exemplary embodiment of the present invention, the storage devicelifetime monitoring method further includes identifying system accesserrors from the system log, application access errors from theapplication log, database access errors from the database log and diskaccess errors in the S.M.A.R.T. log for each of the storage devices;calculating the number of the system access errors, the number of theapplication access errors, the number of the database access errors andthe number of the disk access errors; and establishing the operationactivity information of each of the storage devices according to thenumber of the system access errors, the number of the application accesserrors, the number of the database access errors and the number of thedisk access errors.

In an exemplary embodiment of the present invention, the step ofconstructing the storage device lifetime predicting model according tothe operation activity information and the corresponding operationlifetime value of each of the training data includes constructing thestorage device lifetime predicting model by means of aK-means-clustering algorithm, a linear regression algorithm or an SVM.

In an exemplary embodiment of the present invention, the step ofconstructing the storage device lifetime predicting model according tothe operation activity information and the corresponding operationlifetime value of each of the training data includes splitting thetraining data and a plurality of predicting data composed of theoperation activity information and the predicted lifetime value of eachof the storage devices into a plurality of data sets; constructing aplurality of sub predicting models respectively according to the datasets; and merging the sub predicting models to form the storage devicelifetime predicting model.

To sum up, in the storage device lifetime monitoring system and thestorage device lifetime monitoring method thereof provided by thepresent invention, the operation activity information corresponding to aplurality of storage devices can be identified, the lifetime of eachstorage device can be predicted according to the operation activityinformation of each storage device through the storage device lifetimepredicting model, and the storage device lifetime predicting model canbe further re-constructed according to predicting data composed of theoperation activity information and the predicted lifetime of eachstorage device. Thereby, in the present invention, a great amount oftraining data with low cost can be produced to facilitate in enhancingthe accuracy of predicting the lifetimes of the storage devices, so asto improve the efficiency of managing the storage devices.

In order to make the aforementioned and other features and advantages ofthe invention more comprehensible, several embodiments accompanied withfigures are described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the invention, and are incorporated in and constitute apart of this specification. The drawings illustrate embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a schematic diagram illustrating a data center according to anexemplary embodiment.

FIG. 2 is a schematic diagram illustrating a relation among programcodes of the storage device lifetime monitoring system according to anexemplary embodiment.

FIG. 3 is a schematic diagram illustrating the operation of constructingthe storage device lifetime predicting model by using training data,predicting data and actual data according to an exemplary embodiment.

FIG. 4 and FIG. 5 are schematic diagrams of constructing the storagedevice lifetime predicting model according to an exemplary embodiment.

FIG. 6 and FIG. 7 are schematic diagrams illustrating a self-learningmethod according to an exemplary embodiment.

FIG. 8 is a schematic diagram illustrating the operation of constructingthe storage device lifetime predicting model by using the training dataand the predicting data according to an exemplary embodiment.

FIG. 9 is a flowchart of a storage device lifetime monitoring methodaccording to an exemplary embodiment.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a schematic diagram illustrating a data center according to anexemplary embodiment.

With reference to FIG. 1, in the present exemplary embodiment, a datacenter 10 includes a server 100 and a plurality of storage devices200(0) to 200(N). The server 100 is coupled to storage devices 200(0) to200(N), and configured to monitor device states of the storage devices200(0) to 200(N). Specially, in the present exemplary embodiment, theserver 100 monitors the device state and predicts a lifetime of each ofthe storage devices 200(0) to 200(N) by using a storage device lifetimemonitoring system 320. It should be understood that the presentinvention is not intent to limit the number of the storage devices200(0) to 200(N).

The storage devices 200(0) to 200(N) are configured to store data in thedata center 10. For example, the stored data may include user datatransmitted to the data center 10 by users, system data configured tomanage the data center and backup data corresponding to the user data orthe system data of the data center or data of any type adapted for beingstored in the data center 10, which construes no limitations to thepresent invention. In the present exemplary embodiment, each of thestorage devices 200(0) to 200(N) may be a hard disk drive (HDD) or anon-volatile memory storage device (SSD) of any type.

In the present exemplary embodiment, the server 100 is configured to notonly monitor the lifetimes of the storage devices 200(0) to 200(N), butalso control the allocation of the storage devices 200(0) to 200(N) inthe data center 10. In the present exemplary embodiment, the server 100includes a processing unit 310, a storage device lifetime monitoringsystem 320, a connection interface unit 330 and a memory unit 340.

The processing unit 310 is configured to control the overall operationof the server 100. In the present exemplary embodiment, The processingunit 310, for example, may be a central processing unit, amicro-processor or any other programmable microprocessor, a digitalsignal processor (DSP), a programmable controller, an applicationspecific integrated circuit (ASIC), a programmable logic device (PLD) orthe like. In the present exemplary embodiment, the processing unit 310is a baseboard management controller (BMC), which is in charge of notonly the overall operation of the server 100, but also monitoringinternal information of the data center 10, such as a fan operationstatus, a temperature, or a voltage. Generally, the processing unit 310may be directly integrated on a baseboard of the server 100 or disposedin a form of a card in the server 100.

The connection interface unit 330 is coupled to the processing unit 310,and the processing unit 310 may be connected to the storage devices200(0) to 200(N) through the connection interface unit 330 to accessdata or issue commands. The connection interface unit 330 may complywith, for example, a serial attached SCSI (SAS) standard, a two-wireinterface (TWI) standard, a serial advanced technology attachment (SATA)standard, a parallel advanced technology attachment (PATA) standard, aninstitute of electrical and electronic engineers (IEEE) 1394 standard, aperipheral component interconnect express (PCI Express) standard, auniversal serial bus (USB) standard, an integrated device electronics(IDE) standard or any other physical interface complying with anadaptive standard, which is not limited in the present invention.

In the present exemplary embodiment, the storage device lifetimemonitoring system 320 may include program codes or data stored in astorage unit coupled to the processing unit 310 and implements afunction of monitoring the plurality of storage devices 200(0) to 200(N)in the data center 10. In the present exemplary embodiment, the storageunit may be, for example, a rewritable non-volatile memory, such as ahard disk drive (HDD), an erasable programmable read only memory(EPROM), an electrically erasable programmable read only memory (EEPROM)or a flash memory, or a circuit with a data storage capability. Itshould be noted that the storage unit is also configured to store otherdata of the server 100, e.g., a firmware or a software for managing theserver 100 itself.

The memory unit 340 is coupled to the processing unit 310, andconfigured to temporarily store data of the server 100. In the presentexemplary embodiment, the memory unit 340 may be, for example, avolatile memory, such as a dynamic random access memory (DRAM) or astatic random access memory (SRAM). In the present exemplary embodiment,to implement the functions of the storage device lifetime monitoringsystem, when the server 100 is powered on, the processing unit 310 readsthe program codes from the storage device lifetime monitoring system320, loads the read program codes to the memory unit 340, executes theprogram codes perform a plurality of functions of the server 100. Inother words, the processing unit 310 executes the program codes toperform a storage device lifetime monitoring method used by the server100.

FIG. 2 is a schematic diagram illustrating a relation among programcodes of the storage device lifetime monitoring system according to anexemplary embodiment. The functions of program codes and a databasestored in the storage device lifetime monitoring system 320 andinteractive relations among them will be described with reference toFIG. 2 hereinafter

With reference to FIG. 2, the storage device lifetime monitoring system320 includes program codes and a database 120 for performing a storagedevice lifetime monitoring method. In the present exemplary embodiment,the program codes include a storage device detecting and analyzingmodule 110, a lifetime estimation training module 130 and a lifetimepredicting module 140.

The storage device detecting and analyzing module 110 is configured tocollect operation activity information corresponding to the storagedevices 200(0) to 200(N). In an exemplary embodiment, the storage devicedetecting and analyzing module 100 includes a log collecting module 111and an operation activity identifying module 112.

The log collection module 111 collects at least one operation logcorresponding to each storage device. To be specific, in the presentexemplary embodiment, when each storage device performs any operation,information with respect to the currently performed operation is recodedin the operation log. For instance, if it is assumed that a storagedevice performs a read operation, the storage device records anyinformation with respect to the data reading operation, such as thestart time, the end time and the size and an address of target data andwhether an error occurs during the data reading operation, in anoperation log corresponding to the data reading operation. In thepresent exemplary embodiment, a manner for the log collecting module 111collecting the at least one operation log corresponding to each storagedevice may be, for example, that the processing unit 310 reads therecorded operation log from the storage device, and inputs the operationlog into the log collection module, but the present invention is notlimited thereto. For example, the log collecting module 111 may activelysend a request to the storage device to obtain the operation log.

In the present exemplary embodiment, the operation log includes a systemlog, an application log, a database log and a self-monitoring analysisand report technical log (S.M.A.R.T. log). The system log recordsinformation with respect to the operation system operated with thestorage devices 200(0) to 200(N). The application log recordsinformation with respect to operations of an application accessing thestorage devices 200(0) to 200(N). The database log records informationwith respect to operations of a client terminal accessing the databasestored in the storage devices 200(0) to 200(N). The S.M.A.R.T. logrecords self-monitoring and analyzing technical informationcorresponding to the storage devices among the storage devices 200(0) to200(N) which belong to HDDs.

In the present exemplary embodiment, the operation activity identifyingmodule 112 is configured to analyze the operation log corresponding tothe each storage device to establish the operation activity informationof each storage device. To be specific, the operation activityidentifying module 112 identifies system access errors from the systemlog, application access errors from the application log, database accesserrors from the database log and disk access errors from the S.M.A.R.T.log for each storage device. Additionally, the operation activityidentifying module 112 also calculates the number of the system accesserrors, the number of the application access errors, the number of thedatabase access errors and the number of the disk access errorscorresponding to each storage device and establishes the operationactivity information of each storage device according to the number ofthe system access errors, the number of the application access errors,the number of the database access errors and the number of the diskaccess errors corresponding to each storage device.

For instance, if it is assumed that the log collection module 111collects a system log, an application log, a database log and anS.M.A.R.T. log from the storage device 200(2). The operation activityidentifying module 112 identifies 3 system access errors from the systemlog of the storage device 200(2), 30 application access errors from theapplication log of the storage device 200(2), 300 database access errorsfrom the database log of the storage device 200(2) and 0 disk accesserror from the S.M.A.R.T. log of the storage device 200(2). Theoperation activity identifying module 112 establishes operation activityinformation of the storage device 200(2), e.g., the operation activityidentifying module 112 records the operation activity information of thestorage device 200(2) as “3, 30, 300, 0.”

Moreover, if it is assumed that the log collection module 111 thenfurther identifies 3 system access errors from the system log, 30application access errors from the application log, 300 database accesserrors from the database log and 0 disk access error from the S.M.A.R.T.log which are collected from the storage device 200(2), the operationactivity identifying module 112 updates the operation activityinformation of the storage device 200(2) as “6, 60, 600, 0.” Namely, theoperation activity identifying module 112 continuously updates theoperation activity information of the storage device 200(2).

In the present exemplary embodiment, the storage device lifetimemonitoring system 320 includes the database 120, in which the database120 records a plurality of training data, and each training dataincludes operation activity information and a corresponding operationlifetime value. Here, one training data includes the operation activityinformation and the corresponding operation lifetime value recordedaccording to the usage status of the previously used storage device.Each training data corresponds to the information with respect to thepreviously used storage device. The description related to the operationactivity information is set forth in detail above and will not berepeatedly described hereinafter. As for the corresponding operationlifetime value, it refers to a sum (also referred to as the lifetime) ofan operation time period from a storage device being manufactured tobeing damaged. However, it should be understood that the presentinvention is not limited thereto. Manufacturers may design thedefinition of the corresponding operation lifetime value based ondemands. A unit corresponding an operation lifetime value may be anysuitable time unit, such as hour. For instance, if it is assumed thatone of the training data is “0, 0, 0, 100, 50000,” the training data mayserve to indicate that the operation activity information of a certainstorage device is “0, 0, 0, 100,” and a lifetime of the storage deviceis 50000 hours. It should be noted that the aforementioned format of thetraining data is only an example for description, and construes nolimitations to the present invention.

The lifetime estimation training module 130 is configured to train(i.e., construct) a storage device lifetime predicting model accordingto the operation activity information and the corresponding operationlifetime values of the training data. Furthermore, when designing thestorage device lifetime monitoring system 320, a manufacturer may useoperation activity information and an operation lifetime (whichpreviously occur) of each storage device in an old data center (oranother data center) as a plurality of training data and record thetraining data by using the database 120 of the storage device lifetimemonitoring system 320, such that the lifetime estimation training moduleconstructs the storage device lifetime predicting model according to thetraining data.

The lifetime predicting module 140 is configured to input the operationactivity information of the storage devices 200(0) to 200(N) receivedfrom the storage device detecting and analyzing module 110 into thestorage device lifetime predicting model constructed by the lifetimeestimation training module 130 and generate a predicted lifetime valueof each storage device. The overall concept of the storage devicelifetime monitoring system of the present invention will be describedwith reference to FIG. 3 hereinafter.

FIG. 3 is a schematic diagram illustrating the operation of constructingthe storage device lifetime predicting model by using training data,predicting data and actual data according to an exemplary embodiment.

First, in the present exemplary embodiment, the processing unit 310inputs the training data from the database 120 of the storage devicelifetime monitoring system 320 to the lifetime estimation trainingmodule 130 to construct a storage device lifetime predicting model(i.e., path R301). The lifetime estimation training module 130 providesthe constructed storage device lifetime predicting model to the lifetimepredicting module 140 (i.e., path 303). The constructed storage devicelifetime predicting model may be configured to predict the lifetimes ofthe storage devices 200(0) to 200(N) of the data center 10.

The storage device detecting and analyzing module 110 (instantly)collects the operation activity information of the storage devices200(0) to 200(N) and inputs the operation activity information into thelifetime predicting module 140 (i.e., path R305).

The lifetime predicting module 140 inputs the received operationactivity information of the storage devices 200(0) to 200(N) into thestorage device lifetime predicting model to generate the predictedlifetime value of each storage device. To be specific, the lifetimepredicting module 140 inputs the established corresponding operationactivity information of each storage device into the constructed storagedevice lifetime predicting model to generate the corresponding predictedlifetime value of each storage device. The corresponding predictedlifetime value of each storage device indicates a sum of an operationaltime period corresponding to each storage device predicted by thestorage device lifetime predicting model. For instance, if it is assumedthat the operation activity information of the storage device 200(2) is“6, 60, 600, 0.” The lifetime predicting module 140 inputs the operationactivity information of the storage device 200(2) which is “6, 60, 600,0” into the constructed storage device lifetime predicting model andgenerates a predicted lifetime value of “5000” (hours) corresponding tothe operation activity information of “6, 60, 600, 0.” In other words,in the condition that the operation activity information currentlycorresponding to the storage device 200(2) is “6, 60, 600, 0,” thestorage device lifetime predicting model predicts that the storagedevice 200(2) is capable of being operated for 5000 hours in total.

Then, the lifetime predicting module 140 transmits predicting datacomposed of the operation activity information and the predictedlifetime value of each storage device to the lifetime estimationtraining module 130 (i.e., path R307), to re-construct the storagedevice lifetime predicting model. To be specific, the storage devicelifetime predicting model may be constructed by means of self-learning.For example, the lifetime predicting module 140 may also transmit aplurality of predicting data composed of the operation activityinformation and the predicted lifetime value of each storage device tothe lifetime estimation training module 130 to re-construct the storagedevice lifetime predicting model. Thereby, the lifetime predictingmodule 130 may obtain a great amount data (i.e., the predicting datacorresponding to each storage device) with low cost by means ofself-learning to construct the storage device lifetime predicting model,so as to strengthen the predicting capability of the storage devicelifetime predicting model. The self-learning mechanism of the presentinvention will be described in detail with reference to the drawingsbelow.

In addition, when any storage device among the storage devices 200(0) to200(N) is damaged, the storage device detecting and analyzing module 110transmits actual data composed of the current operation activityinformation and an actual lifetime value of the damaged storage deviceto the lifetime estimation training module 130, so as to re-constructthe storage device lifetime predicting model (i.e., path R309). Forexample, when a storage device (referred to as a first storage devicehereinafter) among the storage devices is damaged, the storage devicedetecting and analyzing module 110 records an actual lifetime value ofthe first storage device and transmits actual data composed of operationactivity information and the actual lifetime value of the first storagedevice to the lifetime estimation training module 130 to re-constructthe storage device lifetime predicting model. In other words, when thefirst storage device is damaged, as described above, the storage devicedetecting and analyzing module 110 serves the operation activityinformation and the actual lifetime value of the first storage device asdata for constructing to re-construct the storage device lifetimepredicting model. Namely, the operation activity information and theoperational (actual) lifetime value of each storage device whichactually occur corresponding to the operation activity information theoperation of each storage device may also be used to construct thestorage device lifetime predicting model. Particularly, in anotherexemplary embodiment, the actual data may also be added into thedatabase 120 for forming new training data.

In this way, the storage device lifetime monitoring system of thepresent exemplary embodiment is capable of providing a great amount ofconstructing data with low cost to construct the storage device lifetimepredicting model, so as to enhance the accuracy of predicting thelifetime. It is to be mentioned that the mechanism of re-constructingthe storage device lifetime predicting model maybe performedperiodically or irregularly, which is not limited in the presentinvention. Functions of the components and interaction therebetween willbe described in more detail with reference to the drawings hereinafter.

It is to be mentioned that in another exemplary embodiment, the server100 may also perform a preventive operation according to the predictedlifetime value corresponding to the storage device 200(2) obtainedthrough the storage device lifetime monitoring system 320. For instance,in this case, it is assumed that the storage device 200(2) is operatedfor 4900 hours, and the predicted lifetime value corresponding to thestorage device 200(2) is 5000 hours. Since the operated time period ofthe storage device 200(2) is close to the predicted lifetime value, theserver 100 may send an alert message. A maintenance technician of thedata center 10 may perform a preventive operation on the storage device200(2) according to the alert message. For example, the maintenancetechnician of the data center 10 may perform a data backup operation ora repair/replace operation on the storage device 200(2). Specially,after the data is backed up, the maintenance technician of the datacenter 10 may also perform a stress test on the storage device 200(2) toobtain the actual total operational time period (i.e., the actuallifetime value) of the storage device 200(2). As described above, afterthe actual lifetime value of the storage device 200(2) is obtained, theactual lifetime value and the operation activity information of thestorage device 200(2) is transmitted to the lifetime estimation trainingmodule 130 to re-construct the storage device lifetime predicting model.

In the present exemplary embodiment, the lifetime estimation trainingmodule 130 constructs the storage device lifetime predicting model bymeans of a linear regression algorithm and the training data, but thepresent invention is not limited thereto. For example, in otherexemplary embodiments, the lifetime estimation training module 130 mayalso construct the storage device lifetime predicting model by means ofan algorithm adapted for machine learning, such as a K-means-clusteringalgorithm or a support vector machine (SVM).

It is to be mentioned that in another exemplary embodiment, a user mayalso input the training data simultaneously into storage device lifetimepredicting models corresponding to a plurality of algorithms. After theconstruction is completed, a plurality of predicted lifetimes may beobtained by means of inputting operation activity information of acertain training data into the constructed storage device lifetimepredicting models, and one of the storage device lifetime predictingmodels with the highest accuracy may be selected according to theobtained predicted lifetime.

FIG. 4 and FIG. 5 are schematic diagrams of constructing the storagedevice lifetime predicting model according to an exemplary embodiment.

In order to simplify the description, it is assumed that each trainingdata (e.g., dots D1 and D2 illustrated in FIG. 4, FIG. 5) includes thenumber of the system access errors and operation lifetime valuecorresponding to the number of the system access errors. The storagedevice detecting and analyzing module 110 only collects the system logof each storage device, calculates the system access errors of eachstorage device to establish variable operation activity information(i.e., the number of the system access errors) corresponding to eachstorage device. The storage device lifetime predicting model isconstructed by means of the linear regression algorithm using thetraining data. In the present exemplary embodiment, the constructedstorage device lifetime predicting model generates a prediction curve(e.g., dotted lines illustrated in FIG. 4 and FIG. 5) of a linearequation in two variables to predict the predicted lifetime value ofeach storage device.

With reference to FIG. 4, the horizontal axis represents the number ofthe system access errors, and the vertical axis represents the lifetime(i.e., a fulltime lifetime cycle in hours, of which the unit is hour) ofeach storage device. For instance, it is assumed that the storage devicelifetime predicting model is constructed by means of the linearregression algorithm, and two sets of training data D1 and D2 areinputted into the storage device lifetime predicting model in thebeginning. A value of the training data D1 is “2, 500,” which representsa storage device having 2 system access error and a lifetime of 500hours. Similarly, a value of the training data D2 is “10, 100,” whichrepresents a storage device having 10 system access error and a lifetimeof 100 hours. The storage device lifetime predicting model generates adualistic prediction curve Y (e.g., the dotted line illustrated in FIG.4) according to the linear regression algorithm and the training data D1and D2.

With reference to FIG. 5, for instance, it is assumed that othertraining data D3 to D10 is further input into the storage devicelifetime predicting model illustrated in FIG. 4. The storage devicelifetime predicting model generates a dualistic prediction curve A(e.g., the dotted line illustrated in FIG. 5) according to the linearregression algorithm and the training data D1 to D10. The process wheredifferent prediction curves are generated due to different training databeing input may be considered as a process where the construction of thestorage device lifetime predicting model continuously varies with theused algorithm and the input training data. In addition, themanufacturer may determine whether the construction of the storagedevice lifetime predicting model is completed under a predeterminedcondition. For example, referring to FIG. 5, the manufacturer may setthat after 10 sets of training data D1 to D10 are input into the storagedevice lifetime predicting model, the prediction curve A generated bythe storage device lifetime predicting model may indicate the completedconstruction of the storage device lifetime predicting model.Thereafter, the completely constructed storage device lifetimepredicting model may be used to predict the lifetime of each storagedevice.

FIG. 6 and FIG. 7 are schematic diagrams illustrating a self-learningmethod according to an exemplary embodiment. In order to simplify thedescription, the method of constructing the storage device lifetimepredicting model used in FIG. 6 and FIG. 7 is the same as the method ofconstructing the storage device lifetime predicting model used in FIG. 4and FIG. 5 and thus, will not repeated hereinafter.

With reference to FIG. 6, for instance, it is assumed that thecompletely constructed storage device lifetime predicting modelgenerates the dualistic prediction curve A (which is presented as thedotted line illustrated in FIG. 6), and the storage devices 200(0),200(1) and 200(2) respectively have 2, 5 and 8 system access errors. Thestorage device detecting and analyzing module 110 correspondinglyestablishes the operation activity information of the storage devices200(0), 200(1) and 200(2) as “2,” “5” and “8.” The lifetime predictingmodule inputs the operation activity information (the numbers of thesystem access errors) of the storage devices into the storage devicelifetime predicting model. The storage device lifetime predicting modeluses the prediction curve A and the numbers of the system access errors(i.e., “2,” “5” and “8”) to generate the predicted lifetime values,“550,” “115” and “40” to respectively indicate the predicted lifetimesof the storage devices 200(0), 200(1) and 200(2). Namely, the operationactivity information the predicted lifetime values corresponding to thestorage devices 200(0), 200(1) and 200(2) respectively constructspredicting data “2, 550,” “5, 115” and “8, 40” corresponding to thestorage devices 200(0), 200(1) and 200(2). Then, the lifetime predictingmodule 140 transmits the predicting data (i.e., “2, 550,” “5, 115” and“8, 40”) to the lifetime estimation training module 130 to re-constructthe storage device lifetime predicting model.

With reference to FIG. 7, after being re-constructed by using thepredicting data (e.g., triangle points, “2, 550,” “5, 115” and “8, 40,”illustrated in FIG. 7), the storage device lifetime predicting modelgenerates a new prediction curve B. The prediction curve B generated byusing the predicting data for the re-construction (i.e., self-learning)may be used to predict the lifetimes of the storage devices. Forinstance, in case the storage device 200(N) encounters 4 system accesserrors, the storage device lifetime predicting model obtains thepredicted lifetime value of the storage device 200(N), which is 275hours, according to the prediction curve B and the number of the systemaccess errors, 4, of the storage device 200(N).

Referring to FIG. 6 again, if the storage device 200(N) encounters the4^(th) system access error, the storage device lifetime predicting modelobtains the predicted lifetime value of the storage device 200(N), whichis “300” (hours), according to the prediction curve A and the number ofthe system access errors, “4,” of the storage device 200(N). Thetraining data among the plurality of training data corresponding to thenumber of the system access errors, “4,” has the corresponding operationlifetime value of “250” (hours), and in the condition that thecorresponding number of the system access errors is also “4,” thepredicted lifetime value “300” predicted according to the predictioncurve B is much closer to the training data (i.e., “250”) than thepredicted lifetime value “275” predicted according to the predictioncurve A. Accordingly, prediction according to the prediction curve B hashigher accuracy than prediction according to the prediction curve A.Namely, the self-learning method of re-constructing the storage devicelifetime predicting model by using the predicting data indeed cancontribute to the prediction accuracy of the storage device lifetimepredicting model.

In the examples illustrated in FIG. 4 through FIG. 7, the storage devicelifetime predicting model performs the construction according to thetraining data having two variables (which is also referred to2-dimensional training data), where one of the variables iscorresponding to the system access errors of each storage device, andthe other variable is corresponding to the lifetime of each storagedevice. However, it should be noted that the present invention is notintent to limit dimensions (i.e., the variables) of the data, such asthe training data, the predicting data or the actual data, forconstructing the storage device lifetime predicting model. For example,in another exemplary embodiment, besides the necessary data dimensions(i.e., the lifetime of each storage device), the dimensions of the datafor constructing the storage device lifetime predicting model may alsoinclude one of or a combination of the number of the application accesserrors, the number of the database access errors and the number of thedisk access errors, or any other adaptive error types corresponding tothe storage device operation activities. Additionally, the number of thevariable types included in the data for constructing the storage devicelifetime predicting model may also be equal to or more than 2. In otherwords, the number of the dimensions of the data for constructing thestorage device lifetime predicting model may be two or more.

It is to be mentioned that since the amount of the training data orpredicting data for constructing the storage device lifetime predictingmodel is large, in an exemplary embodiment, the lifetime estimationtraining module 130 may accelerate the construction of the storagedevice lifetime predicting model by using a split-and-merge method(e.g., a Hadoop MapReduce algorithm).

FIG. 8 is a schematic diagram illustrating the operation of constructingthe storage device lifetime predicting model by using the training dataand the predicting data according to an exemplary embodiment.

With reference to FIG. 8, in the present exemplary embodiment, thelifetime estimation training module 130 splits the training data and thepredicting data (also referred to as a primary data set) into aplurality of data sets (e.g., sub sets a, b and c illustrated in FIG.8), and transmits the sub sets a, b and c (810, 820 and 830)respectively to a plurality of cluster computing servers (i.e., pathR1). Particularly, each cluster computing server may further split thereceived sub sets into other sub sets and transmit the split sub sets toother cluster computing servers. For example, the cluster computingserver 830 splits the sub set c into sub sets c-1 and c-2 and transmitsthe sub sets c-1 and c-2 respectively to the cluster computing servers831 and 832 (i.e., path R2). It should be noted that the primary dataset may also include the actual data.

Then, the cluster computing servers construct a plurality of subpredicting models respectively according to the received sub sets andreturn the sub predicting models to the lifetime estimation trainingmodule 130. Referring to FIG. 8, the cluster computing servers 831 and832 constructs a plurality of sub predicting models respectivelyaccording to the received sub sets c-1 and c-2 and return the subpredicting models to the lifetime estimation training module 130 (i.e.,path R3). The cluster computing server 830 waits and receives theplurality of sub predicting models respectively returned by the clustercomputing servers 831 and 832 to the cluster computing server 830. Afterthe sub predicting models are received, the cluster computing server 830merges the sub predicting models and returns the merged sub predictingmodels to the lifetime estimation training module 130 (i.e., path R4).Similarly, the cluster computing server 810, 820 also constructs aplurality of sub predicting models according to the received sub sets aand b, and returns the sub predicting models to the lifetime estimationtraining module 130 (i.e., path R4). After receiving the trained subpredicting models from the cluster computing servers 810, 820 and 830,the lifetime estimation training module 130 merges the sub predictingmodels to form the storage device lifetime predicting model. In thisway, by means of the split-and-merge method, the data in a great amountmay be split into sub data sets in a smaller amount, which areindependently computed (i.e., a plurality of sub predicting models areconstructed), and after the construction is completed, results (i.e.,the sub predicting models) are merged to form the storage devicelifetime predicting model, so as to reduce resources and times forconstructing the storage device lifetime predicting model.

It is to be mentioned that the storage device lifetime predicting modelmay also be initially trained by the manufacturer before beingmanufactured. Namely, in another exemplary embodiment, the storagedevice lifetime predicting model is initially constructed by usingtraining data of a database before being manufactured. It is notnecessary for the database 120 of the storage device lifetime monitoringsystem 320 to pre-store a great amount of training data to construct thestorage device lifetime predicting model. However, it should be notedthat the storage device lifetime monitoring system 320 still may add theactual data (i.e., the current operation activity information and theactual lifetime value of the damaged storage device among the storagedevices 200(0) to 200(N)) obtained from the storage devices 200(0) to200(N) of the data center 10 into the database 120 and input theobtained actual data into the lifetime estimation training module 130 toretrain the storage device lifetime predicting model.

FIG. 9 is a flowchart of a storage device lifetime monitoring methodaccording to an exemplary embodiment.

Referring to both FIG. 2 and FIG. 9, in step S901, the storage devicedetecting and analyzing module 110 collects the operation activityinformation corresponding to the storage devices 200(0) to 200(N).

In step S903, the storage device lifetime monitoring system 320establishes the database, where the database records a plurality oftraining data, and each training data includes the operation activityinformation and the corresponding operation lifetime value.

In step S905, the lifetime estimation training module 130 constructs astorage device lifetime predicting model according to the operationactivity information and the corresponding operation lifetime value ofeach training data.

In step S90, the lifetime predicting module 140 inputs the operationactivity information of the storage devices 200(0) to 200(N) into thestorage device lifetime predicting model to generate the correspondingpredicted lifetime value corresponding to each storage device.

In step S909, the lifetime predicting module also re-constructs thestorage device lifetime predicting model according to a plurality ofpredicting data composed of the operation activity information and thepredicted lifetime value of each storage device. In this way, thestorage device lifetime monitoring method of the present exemplaryembodiment constructs storage device lifetime predicting model by usingnot only the training data but also the predicting data. Thereby, theefficiency of constructing the storage device lifetime predicting modelcan be dramatically improved, so as to improve the prediction accuracy.

It is to be mentioned that in the present exemplary embodiment, thefunctions of the storage device detecting and analyzing module 110, thelifetime estimation training module 130, the lifetime predicting module140 are implemented in the form of program codes or software, but thepresent invention is not limited thereto. In another exemplaryembodiment, the storage device detecting and analyzing module 110, thelifetime estimation training module 130, the lifetime predicting module140 may also be implemented by using hardware circuits (e.g., circuitunits). For example, the storage device lifetime monitoring system 320may include a storage device detecting and analyzing circuit unitconfigured to implement the function of the storage device detecting andanalyzing module 110, a lifetime estimation training circuit unitconfigured to implement the function of the lifetime estimation trainingmodule 130, a lifetime predicting circuit unit configured to implementthe function of the lifetime predicting module 140 and a storage circuitunit configured to implement the function of the database storing orrecording the training data, the actual data and the predicting data.

In light of the foregoing, the storage device lifetime monitoring systemand the storage device lifetime monitoring method thereof provided bythe present invention can identify the operation activity informationcorresponding to a plurality of storage devices, predict the lifetime ofeach storage device by means of the storage device lifetime predictingmodel according to the operation activity information of the storagedevices, and further re-construct the storage device lifetime predictingmodel according to a plurality of predicting data composed of theoperation activity information and the predicted lifetime of eachstorage device. Thereby, in the present invention, a great amount oftraining data with low cost can be produced to facilitate in enhancingthe accuracy of predicting the lifetimes of the storage devices, so asto improve the efficiency of managing the storage devices.

Although the invention has been described with reference to the aboveembodiments, it will be apparent to one of the ordinary skill in the artthat modifications to the described embodiment may be made withoutdeparting from the spirit of the invention. Accordingly, the scope ofthe invention will be defined by the attached claims not by the abovedetailed descriptions.

What is claimed is:
 1. A storage device lifetime monitoring system formonitoring lifetimes of a plurality of storage devices, the storagedevice lifetime monitoring system comprising: a storage device detectingand analyzing circuit; a database coupled to the storage devicedetecting and analyzing circuit; a lifetime estimation training circuitcoupled to the storage device detecting and analyzing circuit; and alifetime predicting circuit coupled to the storage device detecting andanalyzing circuit and the lifetime estimation training circuit, whereinthe database records a plurality of training data, wherein each of thetraining data comprises training operation activity information and acorresponding training lifetime value, wherein the training operationactivity information of each of the training data is an amount oftraining access errors, wherein the storage device detecting andanalyzing circuit collects current operation activity information ofeach of the storage devices, and the collected current operationactivity information comprises a current amount of access errors of thecorresponding storage device, wherein the lifetime estimation trainingcircuit constructs, according to a plurality of amounts of trainingaccess errors and the plurality of corresponding training lifetimevalues in the plurality of the training data, a storage device lifetimepredicting model, wherein the storage device lifetime predicting modelgenerates a prediction curve, wherein each point of the prediction curveindicates a mapping relation between an amount of access errors and apredicted lifetime value corresponding to the amount of access errors,wherein the lifetime predicting circuit identifies a first currentamount of access errors of a first storage device among the storagedevices according to the first current operation activity information ofthe first storage device, and uses the prediction curve and the firstcurrent amount of access errors to generate a first predicted lifetimevalue corresponding to the first current amount of access errorsaccording to the storage device lifetime predicting model, wherein thestorage device detecting and analyzing circuit identifies a firstcurrent total operated duration of the first storage device, wherein thefirst current total operated duration is a time accumulated when thefirst storage device is operated, wherein the first predicted lifetimevalue is a limit value of the first current total operated duration ofthe first storage device when the first storage device has the firstcurrent amount of access errors, wherein when the current amount ofaccess errors of the first storage device is changed from the firstcurrent amount of access errors to a second current amount of accesserrors, the lifetime predicting circuit uses the prediction curve andthe second current amount of access errors to generate a secondpredicted lifetime value corresponding to the second current amount ofaccess errors according to the storage device lifetime predicting model,and the limit value of the first current total operated duration of thefirst storage device is changed to the second predicted lifetime value,such that the limit value of the first current total operated durationof the first storage device is dynamically updated, by the lifetimepredicting circuit, with the change of the current amount of accesserrors of the first storage device, wherein, when a difference betweenthe first current total operated duration and the limit value of thefirst current total operated duration is less than a predeterminedvalue, the storage device detecting and analyzing circuit sends anotification and performs a preventive operation on the first storagedevice for protecting the stored data in the first storage device,wherein when a first storage device among the storage devices isdamaged, the storage device detecting and analyzing circuit records anactual lifetime value of the first storage device, wherein the lifetimeestimation training circuit re-constructs the storage device lifetimepredicting model according to the operation activity information and theactual lifetime value of the first storage device.
 2. The storage devicelifetime monitoring system according to claim 1, wherein the lifetimeestimation training circuit re-constructs the storage device lifetimepredicting model according to the operation activity information and thepredicted lifetime value of each of the storage devices.
 3. The storagedevice lifetime monitoring system according to claim 1, wherein thestorage device detecting and analyzing circuit comprises a logcollecting circuit and an operation activity identifying circuit, and inthe operation of the storage device detecting and analyzing circuitcollecting the operation activity information corresponding to thestorage devices, the log collecting circuit collects at least oneoperation log corresponding to each of the storage devices, and theoperation activity identifying circuit analyzes the at least oneoperation log of each of the storage devices to establish the operationactivity information of each of the storage devices.
 4. The storagedevice lifetime monitoring system according to claim 3, wherein the atleast one operation log comprises a system log, an application log, adatabase log and a self-monitoring analysis and report technical log(S.M.A.R.T. log).
 5. The storage device lifetime monitoring systemaccording to claim 4, wherein the operation activity identifying circuitidentifies system access errors from the system log, application accesserrors from the application log, database access errors from thedatabase log and disk access errors in the S.M.A.R.T. log for each ofthe storage devices, wherein the operation activity identifying circuitcalculates the amount of the system access errors, the amount of theapplication access errors, the amount of the database access errors andthe amount of the disk access errors, wherein the operation activityidentifying circuit establishes the operation activity information ofeach of the storage devices according to the amount of the system accesserrors, the amount of the application access errors, the amount of thedatabase access errors and the amount of the disk access errors.
 6. Thestorage device lifetime monitoring system according to claim 1, whereinin the operation of the lifetime estimation training circuitconstructing the storage device lifetime predicting model according tothe operation activity information and the corresponding operationlifetime value of each of the training data, the lifetime estimationtraining circuit constructs the storage device lifetime predicting modelby means of a K-means-clustering algorithm, a linear regressionalgorithm or a support vector machine (SVM).
 7. The storage devicelifetime monitoring system according to claim 1, in the operation oflifetime estimation training circuit constructing the storage devicelifetime predicting model according to the plurality of amounts oftraining access errors and the corresponding operation lifetime valuesin the plurality of the training data, the lifetime estimation trainingcircuit splits the training data and a plurality of predicting datacomposed of an amount of access errors and a predicted lifetime value ofeach of the storage devices into a plurality of data sets, wherein thelifetime estimation training circuit constructs a plurality of subpredicting models respectively according to the data sets, wherein thelifetime estimation training circuit merges the sub predicting models toform the storage device lifetime predicting model.
 8. A storage devicelifetime monitoring method, for monitoring lifetimes of a plurality ofstorage devices, the storage device lifetime monitoring methodcomprising: establishing a database, wherein the database records aplurality of training data, wherein each of the training data comprisestraining operation activity information and a corresponding traininglifetime value, wherein the training operation activity information ofeach of the training data is an amount of training access errors;collecting current operation activity information of each of the storagedevices, wherein the collected current operation activity informationcomprises a current amount of access errors of the corresponding storagedevice; constructing, according to a plurality of amounts of trainingaccess errors and the plurality of corresponding training lifetimevalues in the plurality of the training data, a storage device lifetimepredicting model, wherein the storage device lifetime predicting modelgenerates a prediction curve, wherein each point of the prediction curveindicates a mapping relation between a amount of access errors and aplurality of lifetime value corresponding to the amount of accesserrors; identifying a first current amount of access errors of a firststorage device among the storage devices according to the first currentoperation activity information of the first storage device, and usingthe prediction curve and the first current amount of access errors togenerate a first predicted lifetime value corresponding to the firstcurrent amount of access errors according to the storage device lifetimepredicting model; identifying a first current total operated duration ofthe first storage device, wherein the first current total operatedduration is a time accumulated when the first storage device isoperated; wherein the first predicted lifetime value is a limit value ofthe first current total operated duration of the first storage devicewhen the first storage device has the first current amount of accesserrors; when the current amount of access errors of the first storagedevice is changed from the first current amount of access errors to asecond current amount of access errors, using the prediction curve andthe second current amount of access errors to generate a secondpredicted lifetime value corresponding to the second current amount ofaccess errors according to the storage device lifetime predicting model,and the limit value of the first current total operated duration of thefirst storage device is changed to the second predicted lifetime value,such that the limit value of the first current total operated durationof the first storage device is dynamically updated, by the lifetimepredicting circuit, with the change of the current amount of accesserrors of the first storage device; when a difference between the firstcurrent total operated duration and the limit value of the first currenttotal operated duration is less than a predetermined value, sending anotification and performing a preventive operation on the first storagedevice for protecting the stored data in the first storage device; andwhen a first storage device among the storage devices is damaged,recording an actual lifetime value of the first storage device, andre-constructing the storage device lifetime predicting model accordingto the operation activity information and the actual lifetime value ofthe first storage device.
 9. The storage device lifetime monitoringmethod according to claim 8, further comprising: re-constructing thestorage device lifetime predicting model according to the operationactivity information and the predicted lifetime value of each of thestorage devices.
 10. The storage device lifetime monitoring methodaccording to claim 8, wherein the step of collecting the operationactivity information corresponding to the storage devices comprises:collecting at least one operation log corresponding to each of thestorage devices; and analyzing the at least one operation log of each ofthe storage devices to establish the operation activity information ofeach of the storage devices.
 11. The storage device lifetime monitoringmethod according to claim 10, wherein the at least one operation logcomprises a system log, an application log, a database log and aS.M.A.R.T. log.
 12. The storage device lifetime monitoring methodaccording to claim 11, further comprising: identifying system accesserrors from the system log, application access errors from theapplication log, database access errors from the database log and diskaccess errors in the S.M.A.R.T. log for each of the storage devices;calculating the amount of the system access errors, the amount of theapplication access errors, the amount of the database access errors andthe amount of the disk access errors; and establishing the operationactivity information of each of the storage devices according to theamount of the system access errors, the amount of the application accesserrors, the amount of the database access errors and the amount of thedisk access errors.
 13. The storage device lifetime monitoring methodaccording to claim 8, wherein the step of constructing the storagedevice lifetime predicting model according to the operation activityinformation and the corresponding operation lifetime value of each ofthe training data comprises: constructing the storage device lifetimepredicting model by means of a K-means-clustering algorithm, a linearregression algorithm or an SVM.
 14. The storage device lifetimemonitoring method according to claim 8, wherein the step of constructingthe storage device lifetime predicting model according to the pluralityof amounts of training access errors and the plurality of correspondingoperation lifetime values in the plurality of the training datacomprises: splitting the training data and a plurality of predictingdata composed of an amount of access errors and a predicted lifetimevalue of each of the storage devices into a plurality of data sets;constructing a plurality of sub predicting models respectively accordingto the data sets; and merging the sub predicting models to form thestorage device lifetime predicting model.